Some best practices for anticlustering

library(anticlust)

This vignette documents some “best practices” for using the function anticlustering() (and they are mostly applicable to kplus_anticlustering() as well). In many cases, the suggestions pertain to overriding the default values of arguments, which seems to be a difficult decision for users. However, I advise you: Do not stick with the defaults; check out the results of different anticlustering specifications; repeat the process; play around; read the documentation (especially ?anticlustering); change arguments arbitrarily; compare the output. Nothing can break.¹

This document uses somewhat imperative language; nuance and explanations are given in the package documentation, the other vignettes, and the papers by Papenberg and Klau (2021; https://doi.org/10.1037/met0000301) and Papenberg (2023; https://doi.org/10.1111/bmsp.12315). Note that deciding which anticlustering objective to use usually requires substantial content considerations and cannot be reduced to “which one is better”. However, some hints are given below.

• If speed is not an issue (it usually is not), use method = "local-maximum" instead of the default method = "exchange". It is unambiguously better.
• If speed is not an issue (it usually is not), use several repetitions.
• Use standardize = TRUE instead of the default standardize = FALSE.²
• Do not use the default objective = "diversity" when the group sizes are not equal (preferably, use objective = "kplus").
• If you only care about similarity in mean values, use objective = "variance".
• You should (probably) not only care about similarity in mean values: prefer objective = "kplus" over objective = "variance" (or even better: check out the function kplus_anticlustering()).
• If you (only) care about similarity in means and standard deviations, use objective = "kplus" instead of the default objective = "diversity".
• With k-plus anticlustering, always use standardize = TRUE.
• Do not use the function fast_anticlustering() (which is usually slower than anticlustering()).
• For the optimal anticlustering algorithms (i.e., method = "ilp"): Install the SYMPHONY solver instead of the GLPK solver.
• The objective = "dispersion" is the only objective where you should first try out the optimal method instead of a heuristic, i.e., use method = "ilp" or the function optimal_dispersion().³
• If you have a very large⁴ data set, do not use the default objective = "diversity" because computing the distance matrix may overwhelm your computer. Instead use objective = "variance" or objective = "kplus".